OpenRaiser/Intern-Atlas huggingface dataset:
VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition
Venue: arXiv cs.CV 2025
Year: 2025

This paper proposes a multimodal emotion recognition system, VIsual Spoken Textual Additive Net (VISTANet), to classify emotions reflected by input containing image, speech, and text into discrete classes. A new interpretability technique, K-Average Additive exPlanation (KAAP), has been developed that identifies important visual, spoken, and textual features leading to predicting a particular emotion class. The VISTANet fuses information from image, speech, and text modalities using a hybrid of intermediate and late fusion. It automatically adjusts the weights of their intermediate outputs while computing the weighted average. The KAAP technique computes the contribution of each modality and corresponding features toward predicting a particular emotion class. To mitigate the insufficiency of multimodal emotion datasets labelled with discrete emotion classes, we have constructed the IIT-R MMEmoRec dataset consisting of images, corresponding speech and text, and emotion labels ('angry,' 'happy,' 'hate,' and 'sad'). The VISTANet has resulted in an overall emotion recognition accuracy of 80.11% on the IIT-R MMEmoRec dataset using visual, spoken, and textual modalities, outperforming single or dual-modality configurations. The code and data can be accessed at https://github.com/MIntelligence-Group/MMEmoRec.
Normality-Guided Distributional Reinforcement Learning for Continuous Control
Venue: arXiv cs.LG 2025
Year: 2025

Learning a predictive model of the mean return, or value function, plays a critical role in many reinforcement learning algorithms. Distributional reinforcement learning (DRL) has been shown to improve performance by modeling the value distribution, not just the mean. We study the value distribution in several continuous control tasks and find that the learned value distribution is empirically quite close to normal. We design a method that exploits this property, employing variances predicted from a variance network, along with returns, to analytically compute target quantile bars representing a normal for our distributional value function. In addition, we propose a policy update strategy based on the correctness as measured by structural characteristics of the value distribution not present in the standard value function. The approach we outline is compatible with many DRL structures. We use two representative on-policy algorithms, PPO and TRPO, as testbeds. Our method yields statistically significant improvements in 10 out of 16 continuous task settings, while utilizing a reduced number of weights and achieving faster training time compared to an ensemble-based method for quantifying value distribution uncertainty.
Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation
Venue: arXiv cs.LG 2025
Year: 2025

Multi-source domain adaptation (MSDA) addresses the challenge of learning a label prediction function for an unlabeled target domain by leveraging both the labeled data from multiple source domains and the unlabeled data from the target domain. Conventional MSDA approaches often rely on covariate shift or conditional shift paradigms, which assume a consistent label distribution across domains. However, this assumption proves limiting in practical scenarios where label distributions do vary across domains, diminishing its applicability in real-world settings. For example, animals from different regions exhibit diverse characteristics due to varying diets and genetics. Motivated by this, we propose a novel paradigm called latent covariate shift (LCS), which introduces significantly greater variability and adaptability across domains. Notably, it provides a theoretical assurance for recovering the latent cause of the label variable, which we refer to as the latent content variable. Within this new paradigm, we present an intricate causal generative model by introducing latent noises across domains, along with a latent content variable and a latent style variable to achieve more nuanced rendering of observational data. We demonstrate that the latent content variable can be identified up to block identifiability due to its versatile yet distinct causal structure. We anchor our theoretical insights into a novel MSDA method, which learns the label distribution conditioned on the identifiable latent content variable, thereby accommodating more substantial distribution shifts. The proposed approach showcases exceptional performance and efficacy on both simulated and real-world datasets.
The Alignment Problem from a Deep Learning Perspective
Venue: arXiv cs.AI 2025
Year: 2025

In coming years or decades, artificial general intelligence (AGI) may surpass human capabilities across many critical domains. We argue that, without substantial effort to prevent it, AGIs could learn to pursue goals that are in conflict (i.e. misaligned) with human interests. If trained like today's most capable models, AGIs could learn to act deceptively to receive higher reward, learn misaligned internally-represented goals which generalize beyond their fine-tuning distributions, and pursue those goals using power-seeking strategies. We review emerging evidence for these properties. In this revised paper, we include more direct empirical evidence published as of early 2025. AGIs with these properties would be difficult to align and may appear aligned even when they are not. Finally, we briefly outline how the deployment of misaligned AGIs might irreversibly undermine human control over the world, and we review research directions aimed at preventing this outcome.
InviCloak: An End-to-End Approach to Privacy and Performance in Web Content Distribution
Venue: arXiv cs.CR 2025
Year: 2025

In today's web ecosystem, a website that uses a Content Delivery Network (CDN) shares its Transport Layer Security (TLS) private key or session key with the CDN. In this paper, we present the design and implementation of InviCloak, a system that protects the confidentiality and integrity of a user and a website's private communications without changing TLS or upgrading a CDN. InviCloak builds a lightweight but secure and practical key distribution mechanism using the existing DNS infrastructure to distribute a new public key associated with a website's domain name. A web client and a website can use the new key pair to build an encryption channel inside TLS. InviCloak accommodates the current web ecosystem. A website can deploy InviCloak unilaterally without a client's involvement to prevent a passive attacker inside a CDN from eavesdropping on their communications. If a client also installs InviCloak's browser extension, the client and the website can achieve end-to-end confidential and untampered communications in the presence of an active attacker inside a CDN. Our evaluation shows that InviCloak increases the median page load times (PLTs) of realistic web pages from 2.0s to 2.1s, which is smaller than the median PLTs (2.8s) of a state-of-the-art TEE-based solution.
Compressing integer lists with Contextual Arithmetic Trits
Venue: arXiv cs.DB 2025
Year: 2025

Inverted indexes allow to query large databases without needing to search in the database at each query. An important line of research is to construct the most efficient inverted indexes, both in terms of compression ratio and time efficiency. In this article, we show how to use trit encoding, combined with contextual methods for computing inverted indexes. We perform an extensive study of different variants of these methods and show that our method consistently outperforms the Binary Interpolative Method -- which is one of the golden standards in this topic -- with respect to compression size. We apply our methods to a variety of datasets and make available the source code that produced the results, together with all our datasets.
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
Venue: arXiv cs.NE 2025
Year: 2025

Spiking neural networks (SNNs) have drawn much attention for their high energy efficiency and recent advances in classification performance. However, unlike traditional deep learning, the robustness of SNNs to adversarial examples remains underexplored. This work advances the adversarial attack side of SNNs and makes three major contributions. First, we show that successful white-box attacks on SNNs strongly depend on the surrogate gradient estimation technique, even for adversarially trained models. Second, using the best single surrogate gradient estimator, we study the transferability of adversarial examples between SNNs and state-of-the-art architectures such as Vision Transformers (ViTs) and CNNs. Our analysis reveals two major gaps: no existing white-box attack leverages multiple surrogate estimators, and no single attack effectively fools both SNNs and non-SNN models simultaneously. Third, we propose the Mixed Dynamic Spiking Estimation (MDSE) attack, which dynamically combines multiple surrogate gradients to overcome these gaps. MDSE produces adversarial examples that fool both SNN and non-SNN models, achieving up to 91.4% higher effectiveness on SNN/ViT ensembles and a 3x boost on adversarially trained SNN ensembles over Auto-PGD. Experiments span three datasets (CIFAR-10, CIFAR-100, ImageNet) and nineteen classifiers, and we will release code and models upon publication.
Local-Aware Global Attention Network for Person Re-Identification Based on Body and Hand Images
Venue: arXiv cs.CV 2025
Year: 2025

Learning representative, robust and discriminative information from images is essential for effective person re-identification (Re-Id). In this paper, we propose a compound approach for end-to-end discriminative deep feature learning for person Re-Id based on both body and hand images. We carefully design the Local-Aware Global Attention Network (LAGA-Net), a multi-branch deep network architecture consisting of one branch for spatial attention, one branch for channel attention, one branch for global feature representations and another branch for local feature representations. The attention branches focus on the relevant features of the image while suppressing the irrelevant backgrounds. In order to overcome the weakness of the attention mechanisms, equivariant to pixel shuffling, we integrate relative positional encodings into the spatial attention module to capture the spatial positions of pixels. The global branch intends to preserve the global context or structural information. For the the local branch, which intends to capture the fine-grained information, we perform uniform partitioning to generate stripes on the conv-layer horizontally. We retrieve the parts by conducting a soft partition without explicitly partitioning the images or requiring external cues such as pose estimation. A set of ablation study shows that each component contributes to the increased performance of the LAGA-Net. Extensive evaluations on four popular body-based person Re-Id benchmarks and two publicly available hand datasets demonstrate that our proposed method consistently outperforms existing state-of-the-art methods.
Peer Recommendation Interventions for Health-related Social Support: a Feasibility Assessment
Venue: arXiv cs.HC 2025
Year: 2025

Online health communities (OHCs) offer the promise of connecting with supportive peers. Forming these connections first requires finding relevant peers - a process that can be time-consuming. Peer recommendation systems are a computational approach to make finding peers easier during a health journey. By encouraging OHC users to alter their online social networks, peer recommendations could increase available support. But these benefits are hypothetical and based on mixed, observational evidence. To experimentally evaluate the effect of peer recommendations, we conceptualize these systems as health interventions designed to increase specific beneficial connection behaviors. In this paper, we designed a peer recommendation intervention to increase two behaviors: reading about peer experiences and interacting with peers. We conducted an initial feasibility assessment of this intervention by conducting a 12-week field study in which 79 users of CaringBridge received weekly peer recommendations via email. Our results support the usefulness and demand for peer recommendation and suggest benefits to evaluating larger peer recommendation interventions. Our contributions include practical guidance on the development and evaluation of peer recommendation interventions for OHCs.
Exploiting Hybrid Active and Passive Multiple Access via Slotted ALOHA-Driven Backscatter Communications
Venue: arXiv cs.IT 2025
Year: 2025

In conventional backscatter communication (BackCom) systems, time division multiple access (TDMA) and frequency division multiple access (FDMA) are generally adopted for multiuser backscattering due to their simplicity in implementation. However, as the number of backscatter devices (BDs) proliferates, there will be a high overhead under the traditional centralized control techniques, and the inter-user coordination is unaffordable for the passive BDs, which are of scarce concern in existing works and remain unsolved. To this end, in this paper, we propose a slotted ALOHA-based random access for BackCom systems, in which each BD is randomly chosen and is allowed to coexist with one active device for hybrid multiple access. To excavate and evaluate the performance, a resource allocation problem for max-min transmission rate is formulated, where transmit antenna selection, receive beamforming design, reflection coefficient adjustment, power control, and access probability determination are jointly considered. To deal with this intractable problem, we first transform the objective function with the max-min form into an equivalent linear one, and then decompose the resulting problem into three sub-problems. Next, a block coordinate descent (BCD)-based greedy algorithm with a penalty function, successive convex approximation, and linear programming are designed to obtain sub-optimal solutions for tractable analysis. Simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of transmission rate and fairness.
Defining and Characterizing Reward Hacking
Venue: arXiv cs.LG 2025
Year: 2025

We provide the first formal definition of reward hacking, a phenomenon where optimizing an imperfect proxy reward function leads to poor performance according to the true reward function. We say that a proxy is unhackable if increasing the expected proxy return can never decrease the expected true return. Intuitively, it might be possible to create an unhackable proxy by leaving some terms out of the reward function (making it "narrower") or overlooking fine-grained distinctions between roughly equivalent outcomes, but we show this is usually not the case. A key insight is that the linearity of reward (in state-action visit counts) makes unhackability a very strong condition. In particular, for the set of all stochastic policies, two reward functions can only be unhackable if one of them is constant. We thus turn our attention to deterministic policies and finite sets of stochastic policies, where non-trivial unhackable pairs always exist, and establish necessary and sufficient conditions for the existence of simplifications, an important special case of unhackability. Our results reveal a tension between using reward functions to specify narrow tasks and aligning AI systems with human values.
On the Descriptive Complexity of Groups without Abelian Normal Subgroups
Venue: arXiv cs.LO 2025
Year: 2025

In this paper, we explore the descriptive complexity theory of finite groups by examining the power of the second Ehrenfeucht--Fraïssé bijective pebble game in Hella's (Ann. Pure Appl. Log., 1989) hierarchy. This is a Spoiler--Duplicator game in which Spoiler can place up to two pebbles each round. While it trivially solves graph isomorphism, it may be nontrivial for finite groups, and other ternary relational structures. We first provide a novel generalization of Weisfeiler--Leman (WL) coloring, which we call 2-ary WL. We then show that 2-ary WL is equivalent to the second Ehrenfeucht--Fraïssé bijective pebble game in Hella's hierarchy. Our main result is that, in the pebble game characterization, only $O(1)$ pebbles and $O(1)$ rounds are sufficient to identify all groups without Abelian normal subgroups (a class of groups for which isomorphism testing is known to be in $\mathsf{P}$; Babai, Codenotti, & Qiao, ICALP 2012). We actually show that $7$ pebbles and $7$ rounds suffice. In particular, we show that within the first few rounds, Spoiler can force Duplicator to select an isomorphism between two such groups at each subsequent round. By Hella's results (ibid.), this is equivalent to saying that these groups are identified by formulas in first-order logic with generalized 2-ary quantifiers, using only $7$ variables and $7$ quantifier depth.
Signed Latent Factors for Spamming Activity Detection
Venue: arXiv cs.IR 2025
Year: 2025

Due to the increasing trend of performing spamming activities (e.g., Web spam, deceptive reviews, fake followers, etc.) on various online platforms to gain undeserved benefits, spam detection has emerged as a hot research issue. Previous attempts to combat spam mainly employ features related to metadata, user behaviors, or relational ties. These studies have made considerable progress in understanding and filtering spamming campaigns. However, this problem remains far from fully solved. Almost all the proposed features focus on a limited number of observed attributes or explainable phenomena, making it difficult for existing methods to achieve further improvement. To broaden the vision about solving the spam problem and address long-standing challenges (class imbalance and graph incompleteness) in the spam detection area, we propose a new attempt of utilizing signed latent factors to filter fraudulent activities. The spam-contaminated relational datasets of multiple online applications in this scenario are interpreted by the unified signed network. Two competitive and highly dissimilar algorithms of latent factors mining (LFM) models are designed based on multi-relational likelihoods estimation (LFM-MRLE) and signed pairwise ranking (LFM-SPR), respectively. We then explore how to apply the mined latent factors to spam detection tasks. Experiments on real-world datasets of different kinds of Web applications (social media and Web forum) indicate that LFM models outperform state-of-the-art baselines in detecting spamming activities. By specifically manipulating experimental data, the effectiveness of our methods in dealing with incomplete and imbalanced challenges is validated.
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations
Venue: arXiv cs.NI 2025
Year: 2025

In edge computing, users' service profiles are migrated due to user mobility. Reinforcement learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like autonomous driving and real-time obstacle detection. Nevertheless, these failures (rare events), being not adequately represented in historical training data, pose a challenge for data-driven RL algorithms. As it is impractical to adjust failure frequency in real-world applications for training, we introduce FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. FIRE considers delay, migration, failure, and backup placement costs across individual and shared service profiles. We prove ImRE's boundedness and convergence to optimality. Next, we introduce novel deep Q-learning (ImDQL) and actor critic (ImACRE) versions of our algorithm to enhance scalability. We extend our framework to accommodate users with varying risk tolerances. Through trace driven experiments, we show that FIRE reduces costs compared to vanilla RL and the greedy baseline in the event of failures.
Experiential Explanations for Reinforcement Learning
Venue: arXiv cs.AI 2025
Year: 2025

Reinforcement learning (RL) systems can be complex and non-interpretable, making it challenging for non-AI experts to understand or intervene in their decisions. This is due in part to the sequential nature of RL in which actions are chosen because of their likelihood of obtaining future rewards. However, RL agents discard the qualitative features of their training, making it difficult to recover user-understandable information for "why" an action is chosen. We propose a technique Experiential Explanations to generate counterfactual explanations by training influence predictors along with the RL policy. Influence predictors are models that learn how different sources of reward affect the agent in different states, thus restoring information about how the policy reflects the environment. Two human evaluation studies revealed that participants presented with Experiential Explanations were better able to correctly guess what an agent would do than those presented with other standard types of explanation. Participants also found that Experiential Explanations are more understandable, satisfying, complete, useful, and accurate. Qualitative analysis provides information on the factors of Experiential Explanations that are most useful and the desired characteristics that participants seek from the explanations.
Obvious Independence of Clones
Venue: arXiv cs.GT 2025
Year: 2025

The Independence of Clones (IoC) criterion measures a voting rule's robustness to strategic nomination. Prior literature has established empirically that individuals may still submit costly, distortionary misreports even in strategy-proof (SP) settings, due to failure to recognize the SP property. The intersection of these issues motivates the search for mechanisms that are Obviously Independent of Clones (OIoC): where strategic nomination/exiting of clones obviously has no effect on the outcome. We construct a formal and intuitive definition of a voting rule being OIoC and examine five IoC rules to identify whether they satisfy OIoC.
Self-supervised video pretraining yields robust and more human-aligned visual representations
Venue: arXiv cs.CV 2025
Year: 2025

Humans learn powerful representations of objects and scenes by observing how they evolve over time. Yet, outside of specific tasks that require explicit temporal understanding, static image pretraining remains the dominant paradigm for learning visual foundation models. We question this mismatch, and ask whether video pretraining can yield visual representations that bear the hallmarks of human perception: generalisation across tasks, robustness to perturbations, and consistency with human judgements. To that end we propose a novel procedure for curating videos, and develop a contrastive framework which learns from the complex transformations therein. This simple paradigm for distilling knowledge from videos, called VITO, yields general representations that far outperform prior video pretraining methods on image understanding tasks, and image pretraining methods on video understanding tasks. Moreover, VITO representations are significantly more robust to natural and synthetic deformations than image-, video-, and adversarially-trained ones. Finally, VITO's predictions are strongly aligned with human judgements, surpassing models that were specifically trained for that purpose. Together, these results suggest that video pretraining could be a simple way of learning unified, robust, and human-aligned representations of the visual world.